Who misperceives the foreign-born share in Europe? A machine-learning approach

Kevin Denny

No 202609, Working Papers from School of Economics, University College Dublin

Abstract: Citizens of developed democracies systematically overestimate the size of their foreign-born population. A now substantial comparative literature has converged on a familiar set of correlates: education, ideology, media exposure, inter-group contact and country-level actual immigrant share. Almost without exception, this literature has used multilevel OLS or logistic regression on a pre-specified set of covariates, with little discipline over which marginal predictors enter the model. I apply Lasso-based variable selection, including post-double selection inference for a set of focal predictors, to a rich candidate set of 35 individual-level variables drawn from a large European cross-national survey. Of these, 26 are robustly selected. The canonical predictors survive, but the analysis also highlights three results that have been under-emphasised in prior work: depressive symptoms are a robust correlate of overestimation; social trust rivals education as a calibrating influence; and general television consumption, not political news viewing, accounts for the measured media effect.

Keywords: Immigration attitudes; Misperception; Innumeracy; Machine learning; Lasso (search for similar items in EconPapers)
JEL-codes: C55 D83 J15 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2026-05
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